Cooperative target localization using multiple UAVs with out-of-sequence measurements

Abstract

Purpose The time delay would occurs when the measurements of multiple unmanned aerial vehicles (UAVs) are transmitted to the date processing center during cooperative target localization. This problem is often named as the out-of-sequence measurement (OOSM) problem. This paper aims to present a nonlinear filtering based on solving the Fokker–Planck equation to address the issue of OOSM. Design/methodology/approach According to the arrival time of measurement, the proposed nonlinear filtering can be divided into two parts. The non-delay measurement would be fused in the first part, in which the Fokker–Planck equation is utilized to propagate the conditional probability density function in the forward form. The time delay measurement is fused in the second part, in which the Fokker–Planck is used in the backward form approximately. The Bayes formula is applied in both parts during the measurement update. Findings Under the Bayesian filtering framework, this nonlinear filtering is not only suitable for the Gaussian noise assumption but also for the non-Gaussian noise assumption. The nonlinear filtering is applied to the cooperative target localization problem. Simulation results show that the proposed filtering algorithm is superior to the previous Y algorithm. Practical implications In this paper, the research shows that a better performance can be obtained by fusing multiple UAV measurements and treating time delay in measurement with the proposed algorithm. Originality/value In this paper, the OOSM problem is settled based on solving the Fokker–Planck equation. Generally, the Fokker–Planck equation can be used to predict the probability density forward in time. However, to associate the current state with the state related to OOSM, it would be used to propagate the probability density backward either.

Research status of multi - robot systems task allocation and uncertainty treatment

“…In addition, when the UAVs perform cooperative target positioning, the measurement data transmission will be delayed. Wang, Qin, Bai and Cui [36] proposed a nonlinear filter based on solving the Fokker-Planck equation to solve this problem. According to the measured arrival time, the proposed nonlinear filter can be divided into two parts.…”

Equalized Recovery State Estimators for Linear Systems with Delayed and Missing Observations

“…Literature review: For the past few decades, active research development has been undertaken in the area of state estimation for systems that are susceptible to packet drops and delayed communication, as highlighted in [1], [2] as typical concerns in networked control systems. Significant amount of research has been done to design state estimators when only intermittent data is available [3], [4], [5], and when observations are arriving as out-of-sequence measurements [6], [7], [8], [9]. The authors in [6] used complete in-sequence information approach to recompute all the estimations from the step when data did not arrive until the point when it finally arrived, while [7] proposed nonlinear filters utilizing a Bayesian filtering framework to correct the previous estimation as soon as the delayed observation arrives.…”

“…Significant amount of research has been done to design state estimators when only intermittent data is available [3], [4], [5], and when observations are arriving as out-of-sequence measurements [6], [7], [8], [9]. The authors in [6] used complete in-sequence information approach to recompute all the estimations from the step when data did not arrive until the point when it finally arrived, while [7] proposed nonlinear filters utilizing a Bayesian filtering framework to correct the previous estimation as soon as the delayed observation arrives. On the other hand, an optimal state estimation approach was proposed for Markovian jump linear systems subject to delays in both the output and mode observations in [8].…”

Genetic Resampling Particle Filter with Randomly Delayed Measurements and Its Application to Line-of-Sight Rate Estimation

“…Therefore, many studies have been conducted, and the most common ones were proposed based on backward prediction and forward prediction. [14][15][16] These filters perform well coping with delayed measurements, but the latency time is indispensable. Inspired by the development of Gaussian filters coping with randomly delayed measurements, [17][18][19] the paper reformulates the weight update equation based on the modified measurement model and then the RD-GRPF is obtained.…”